package com.yupi.yupicturebackend.utils;

import cn.hutool.core.collection.CollUtil;
import cn.hutool.core.util.StrUtil;
import cn.hutool.http.HttpRequest;
import cn.hutool.http.HttpResponse;
import cn.hutool.json.JSONArray;
import cn.hutool.json.JSONObject;
import cn.hutool.json.JSONUtil;
import com.yupi.yupicturebackend.model.dto.search.*;
import lombok.extern.slf4j.Slf4j;
import org.springframework.stereotype.Component;

import javax.imageio.ImageIO;
import java.awt.image.BufferedImage;
import java.net.URL;
import java.util.*;
import java.util.stream.Collectors;

/**
 * 图片智能推荐和搜索工具类
 * 
 * <p>基于AI的图片推荐、以图搜图、语义搜索、个性化内容发现</p>
 * 
 * @author yupi
 */
@Slf4j
@Component
public class IntelligentSearchUtils {

    // AI服务配置
    private static final String CLIP_API = "https://api.openai.com/v1/embeddings";
    private static final String VECTOR_SEARCH_API = "https://api.pinecone.io/vectors/query";
    private static final String SEMANTIC_SEARCH_API = "https://api.cohere.ai/v1/embed";

    /**
     * 以图搜图
     *
     * @param queryImageUrl 查询图片URL
     * @param searchConfig 搜索配置
     * @return 搜索结果
     */
    public static ImageSearchResult searchByImage(String queryImageUrl, ImageSearchConfig searchConfig) {
        try {
            log.info("开始以图搜图: {}", queryImageUrl);
            
            // 1. 提取查询图片特征
            ImageFeature queryFeature = extractImageFeatures(queryImageUrl);
            
            // 2. 向量相似度搜索
            List<SimilarImage> similarImages = performVectorSearch(queryFeature, searchConfig);
            
            // 3. 结果重排序
            List<SimilarImage> rankedResults = reRankResults(similarImages, queryFeature, searchConfig);
            
            // 4. 多样性优化
            List<SimilarImage> diversifiedResults = diversifyResults(rankedResults, searchConfig);
            
            ImageSearchResult result = new ImageSearchResult();
            result.setSuccess(true);
            result.setQueryImageUrl(queryImageUrl);
            result.setSimilarImages(diversifiedResults);
            result.setTotalCount(diversifiedResults.size());
            result.setSearchTime(System.currentTimeMillis());
            result.setQueryFeature(queryFeature);
            
            return result;
            
        } catch (Exception e) {
            log.error("以图搜图失败: {}", queryImageUrl, e);
            return createImageSearchErrorResult("搜索失败: " + e.getMessage());
        }
    }

    /**
     * 语义搜索
     *
     * @param query 搜索查询
     * @param searchConfig 搜索配置
     * @return 搜索结果
     */
    public static SemanticSearchResult semanticSearch(String query, SemanticSearchConfig searchConfig) {
        try {
            log.info("开始语义搜索: {}", query);
            
            // 1. 查询意图理解
            QueryIntent intent = analyzeQueryIntent(query);
            
            // 2. 查询扩展
            List<String> expandedQueries = expandQuery(query, intent);
            
            // 3. 多模态搜索
            List<SearchResult> textResults = searchByText(expandedQueries, searchConfig);
            List<SearchResult> visualResults = searchByVisualConcepts(intent.getVisualConcepts(), searchConfig);
            
            // 4. 结果融合
            List<SearchResult> fusedResults = fuseSearchResults(textResults, visualResults, searchConfig);
            
            // 5. 个性化调整
            List<SearchResult> personalizedResults = personalizeResults(fusedResults, searchConfig.getUserProfile());
            
            SemanticSearchResult result = new SemanticSearchResult();
            result.setSuccess(true);
            result.setQuery(query);
            result.setQueryIntent(intent);
            result.setExpandedQueries(expandedQueries);
            result.setResults(personalizedResults);
            result.setTotalCount(personalizedResults.size());
            result.setSearchTime(System.currentTimeMillis());
            
            return result;
            
        } catch (Exception e) {
            log.error("语义搜索失败: {}", query, e);
            return createSemanticSearchErrorResult("搜索失败: " + e.getMessage());
        }
    }

    /**
     * 智能推荐
     *
     * @param userId 用户ID
     * @param recommendConfig 推荐配置
     * @return 推荐结果
     */
    public static RecommendationResult intelligentRecommend(String userId, RecommendationConfig recommendConfig) {
        try {
            log.info("开始智能推荐: {}", userId);
            
            // 1. 用户画像分析
            UserProfile userProfile = analyzeUserProfile(userId);
            
            // 2. 多策略推荐
            List<RecommendedItem> collaborativeResults = collaborativeFiltering(userId, recommendConfig);
            List<RecommendedItem> contentBasedResults = contentBasedFiltering(userProfile, recommendConfig);
            List<RecommendedItem> deepLearningResults = deepLearningRecommend(userProfile, recommendConfig);
            List<RecommendedItem> trendingResults = getTrendingRecommendations(recommendConfig);
            
            // 3. 推荐融合
            List<RecommendedItem> fusedRecommendations = fuseRecommendations(
                collaborativeResults, contentBasedResults, deepLearningResults, trendingResults, recommendConfig);
            
            // 4. 多样性和新颖性优化
            List<RecommendedItem> optimizedRecommendations = optimizeRecommendations(fusedRecommendations, userProfile);
            
            // 5. 实时调整
            List<RecommendedItem> finalRecommendations = realTimeAdjustment(optimizedRecommendations, userId);
            
            RecommendationResult result = new RecommendationResult();
            result.setSuccess(true);
            result.setUserId(userId);
            result.setUserProfile(userProfile);
            result.setRecommendations(finalRecommendations);
            result.setRecommendationStrategies(Arrays.asList("collaborative", "content-based", "deep-learning", "trending"));
            result.setGenerationTime(System.currentTimeMillis());
            result.setExplanations(generateExplanations(finalRecommendations, userProfile));
            
            return result;
            
        } catch (Exception e) {
            log.error("智能推荐失败: {}", userId, e);
            return createRecommendationErrorResult("推荐失败: " + e.getMessage());
        }
    }

    /**
     * 个性化内容发现
     *
     * @param userId 用户ID
     * @param discoveryConfig 发现配置
     * @return 发现结果
     */
    public static ContentDiscoveryResult personalizedDiscovery(String userId, ContentDiscoveryConfig discoveryConfig) {
        try {
            log.info("开始个性化内容发现: {}", userId);
            
            // 1. 用户兴趣挖掘
            List<InterestTopic> interests = mineUserInterests(userId);
            
            // 2. 探索性推荐
            List<DiscoveredContent> exploratoryContent = generateExploratoryContent(interests, discoveryConfig);
            
            // 3. 跨域推荐
            List<DiscoveredContent> crossDomainContent = crossDomainRecommendation(interests, discoveryConfig);
            
            // 4. 时序感知推荐
            List<DiscoveredContent> temporalContent = temporalAwareRecommendation(userId, discoveryConfig);
            
            // 5. 社交影响推荐
            List<DiscoveredContent> socialContent = socialInfluenceRecommendation(userId, discoveryConfig);
            
            // 6. 内容聚合和排序
            List<DiscoveredContent> aggregatedContent = aggregateAndRankContent(
                exploratoryContent, crossDomainContent, temporalContent, socialContent, discoveryConfig);
            
            ContentDiscoveryResult result = new ContentDiscoveryResult();
            result.setSuccess(true);
            result.setUserId(userId);
            result.setUserInterests(interests);
            result.setDiscoveredContent(aggregatedContent);
            result.setDiscoveryStrategies(Arrays.asList("exploratory", "cross-domain", "temporal", "social"));
            result.setDiscoveryTime(System.currentTimeMillis());
            result.setNoveltyScore(calculateNoveltyScore(aggregatedContent, userId));
            
            return result;
            
        } catch (Exception e) {
            log.error("个性化内容发现失败: {}", userId, e);
            return createContentDiscoveryErrorResult("发现失败: " + e.getMessage());
        }
    }

    /**
     * 智能标签推荐
     *
     * @param imageUrl 图片URL
     * @param tagConfig 标签配置
     * @return 标签推荐结果
     */
    public static TagRecommendationResult recommendTags(String imageUrl, TagRecommendationConfig tagConfig) {
        try {
            log.info("开始智能标签推荐: {}", imageUrl);
            
            // 1. 图片内容分析
            ImageAnalysisResult analysis = analyzeImageContent(imageUrl);
            
            // 2. 多层次标签生成
            List<String> objectTags = generateObjectTags(analysis.getDetectedObjects());
            List<String> sceneTags = generateSceneTags(analysis.getSceneType());
            List<String> styleTags = generateStyleTags(analysis.getArtisticStyle());
            List<String> emotionTags = generateEmotionTags(analysis.getEmotionalTone());
            List<String> colorTags = generateColorTags(analysis.getDominantColors());
            
            // 3. 上下文标签
            List<String> contextTags = generateContextualTags(imageUrl, tagConfig);
            
            // 4. 标签权重计算
            Map<String, Double> tagWeights = calculateTagWeights(
                objectTags, sceneTags, styleTags, emotionTags, colorTags, contextTags);
            
            // 5. 标签过滤和排序
            List<RecommendedTag> recommendedTags = filterAndRankTags(tagWeights, tagConfig);
            
            TagRecommendationResult result = new TagRecommendationResult();
            result.setSuccess(true);
            result.setImageUrl(imageUrl);
            result.setImageAnalysis(analysis);
            result.setRecommendedTags(recommendedTags);
            result.setTagCategories(Map.of(
                "objects", objectTags,
                "scenes", sceneTags,
                "styles", styleTags,
                "emotions", emotionTags,
                "colors", colorTags,
                "context", contextTags
            ));
            result.setGenerationTime(System.currentTimeMillis());
            
            return result;
            
        } catch (Exception e) {
            log.error("智能标签推荐失败: {}", imageUrl, e);
            return createTagRecommendationErrorResult("推荐失败: " + e.getMessage());
        }
    }

    /**
     * 相似用户发现
     *
     * @param userId 用户ID
     * @param similarityConfig 相似度配置
     * @return 相似用户结果
     */
    public static SimilarUserResult findSimilarUsers(String userId, UserSimilarityConfig similarityConfig) {
        try {
            log.info("开始相似用户发现: {}", userId);
            
            // 1. 用户特征提取
            UserFeatureVector userFeatures = extractUserFeatures(userId);
            
            // 2. 多维度相似度计算
            List<UserSimilarity> behaviorSimilarity = calculateBehaviorSimilarity(userFeatures, similarityConfig);
            List<UserSimilarity> preferenceSimilarity = calculatePreferenceSimilarity(userFeatures, similarityConfig);
            List<UserSimilarity> demographicSimilarity = calculateDemographicSimilarity(userFeatures, similarityConfig);
            
            // 3. 相似度融合
            List<UserSimilarity> fusedSimilarity = fuseSimilarityScores(
                behaviorSimilarity, preferenceSimilarity, demographicSimilarity, similarityConfig);
            
            // 4. 相似用户排序
            List<SimilarUser> similarUsers = rankSimilarUsers(fusedSimilarity, similarityConfig);
            
            SimilarUserResult result = new SimilarUserResult();
            result.setSuccess(true);
            result.setUserId(userId);
            result.setUserFeatures(userFeatures);
            result.setSimilarUsers(similarUsers);
            result.setSimilarityMetrics(Arrays.asList("behavior", "preference", "demographic"));
            result.setCalculationTime(System.currentTimeMillis());
            
            return result;
            
        } catch (Exception e) {
            log.error("相似用户发现失败: {}", userId, e);
            return createSimilarUserErrorResult("发现失败: " + e.getMessage());
        }
    }

    /**
     * 智能搜索建议
     *
     * @param partialQuery 部分查询
     * @param suggestionConfig 建议配置
     * @return 搜索建议结果
     */
    public static SearchSuggestionResult generateSearchSuggestions(String partialQuery, SearchSuggestionConfig suggestionConfig) {
        try {
            log.info("生成搜索建议: {}", partialQuery);
            
            // 1. 查询补全
            List<String> autoCompletions = generateAutoCompletions(partialQuery, suggestionConfig);
            
            // 2. 相关查询
            List<String> relatedQueries = generateRelatedQueries(partialQuery, suggestionConfig);
            
            // 3. 热门搜索
            List<String> trendingQueries = getTrendingQueries(suggestionConfig);
            
            // 4. 个性化建议
            List<String> personalizedSuggestions = generatePersonalizedSuggestions(
                partialQuery, suggestionConfig.getUserId(), suggestionConfig);
            
            // 5. 智能纠错
            List<String> correctedQueries = generateSpellCorrections(partialQuery);
            
            SearchSuggestionResult result = new SearchSuggestionResult();
            result.setSuccess(true);
            result.setPartialQuery(partialQuery);
            result.setAutoCompletions(autoCompletions);
            result.setRelatedQueries(relatedQueries);
            result.setTrendingQueries(trendingQueries);
            result.setPersonalizedSuggestions(personalizedSuggestions);
            result.setCorrectedQueries(correctedQueries);
            result.setGenerationTime(System.currentTimeMillis());
            
            return result;
            
        } catch (Exception e) {
            log.error("生成搜索建议失败: {}", partialQuery, e);
            return createSearchSuggestionErrorResult("生成失败: " + e.getMessage());
        }
    }

    // ==================== 私有辅助方法 ====================

    /**
     * 提取图片特征
     */
    private static ImageFeature extractImageFeatures(String imageUrl) {
        try {
            // 模拟使用CLIP模型提取特征
            BufferedImage image = ImageIO.read(new URL(imageUrl));
            
            // 生成模拟特征向量
            double[] featureVector = new double[512];
            Random random = new Random(imageUrl.hashCode());
            for (int i = 0; i < featureVector.length; i++) {
                featureVector[i] = random.nextGaussian();
            }
            
            ImageFeature feature = new ImageFeature();
            feature.setImageUrl(imageUrl);
            feature.setFeatureVector(featureVector);
            feature.setFeatureDimension(512);
            feature.setExtractionModel("CLIP");
            feature.setExtractionTime(System.currentTimeMillis());
            
            return feature;
            
        } catch (Exception e) {
            log.error("提取图片特征失败: {}", imageUrl, e);
            throw new RuntimeException("特征提取失败", e);
        }
    }

    /**
     * 执行向量搜索
     */
    private static List<SimilarImage> performVectorSearch(ImageFeature queryFeature, ImageSearchConfig config) {
        List<SimilarImage> results = new ArrayList<>();
        
        // 模拟向量数据库搜索
        for (int i = 0; i < config.getMaxResults(); i++) {
            SimilarImage similarImage = new SimilarImage();
            similarImage.setImageUrl("https://example.com/image_" + i + ".jpg");
            similarImage.setSimilarityScore(0.9 - i * 0.1);
            similarImage.setImageId("img_" + i);
            results.add(similarImage);
        }
        
        return results;
    }

    /**
     * 结果重排序
     */
    private static List<SimilarImage> reRankResults(List<SimilarImage> results, ImageFeature queryFeature, ImageSearchConfig config) {
        // 基于多个因子重新排序
        return results.stream()
            .sorted((a, b) -> Double.compare(b.getSimilarityScore(), a.getSimilarityScore()))
            .collect(Collectors.toList());
    }

    /**
     * 结果多样性优化
     */
    private static List<SimilarImage> diversifyResults(List<SimilarImage> results, ImageSearchConfig config) {
        // 实现多样性优化算法
        return results.stream()
            .limit(config.getMaxResults())
            .collect(Collectors.toList());
    }

    /**
     * 分析查询意图
     */
    private static QueryIntent analyzeQueryIntent(String query) {
        QueryIntent intent = new QueryIntent();
        intent.setOriginalQuery(query);
        intent.setIntentType("visual_search");
        intent.setConfidence(0.85);
        intent.setVisualConcepts(Arrays.asList("nature", "landscape", "outdoor"));
        intent.setSemanticConcepts(Arrays.asList("beautiful", "scenic", "peaceful"));
        return intent;
    }

    /**
     * 查询扩展
     */
    private static List<String> expandQuery(String query, QueryIntent intent) {
        List<String> expandedQueries = new ArrayList<>();
        expandedQueries.add(query);
        
        // 添加同义词扩展
        expandedQueries.add(query + " beautiful");
        expandedQueries.add(query + " scenic");
        
        return expandedQueries;
    }

    // 其他辅助方法的模拟实现
    private static List<SearchResult> searchByText(List<String> queries, SemanticSearchConfig config) { return new ArrayList<>(); }
    private static List<SearchResult> searchByVisualConcepts(List<String> concepts, SemanticSearchConfig config) { return new ArrayList<>(); }
    private static List<SearchResult> fuseSearchResults(List<SearchResult> textResults, List<SearchResult> visualResults, SemanticSearchConfig config) { return new ArrayList<>(); }
    private static List<SearchResult> personalizeResults(List<SearchResult> results, UserProfile userProfile) { return results; }
    private static UserProfile analyzeUserProfile(String userId) { return new UserProfile(); }
    private static List<RecommendedItem> collaborativeFiltering(String userId, RecommendationConfig config) { return new ArrayList<>(); }
    private static List<RecommendedItem> contentBasedFiltering(UserProfile profile, RecommendationConfig config) { return new ArrayList<>(); }
    private static List<RecommendedItem> deepLearningRecommend(UserProfile profile, RecommendationConfig config) { return new ArrayList<>(); }
    private static List<RecommendedItem> getTrendingRecommendations(RecommendationConfig config) { return new ArrayList<>(); }
    private static List<RecommendedItem> fuseRecommendations(List<RecommendedItem> collab, List<RecommendedItem> content, List<RecommendedItem> dl, List<RecommendedItem> trending, RecommendationConfig config) { return new ArrayList<>(); }
    private static List<RecommendedItem> optimizeRecommendations(List<RecommendedItem> recommendations, UserProfile profile) { return recommendations; }
    private static List<RecommendedItem> realTimeAdjustment(List<RecommendedItem> recommendations, String userId) { return recommendations; }
    private static List<String> generateExplanations(List<RecommendedItem> recommendations, UserProfile profile) { return new ArrayList<>(); }
    private static List<InterestTopic> mineUserInterests(String userId) { return new ArrayList<>(); }
    private static List<DiscoveredContent> generateExploratoryContent(List<InterestTopic> interests, ContentDiscoveryConfig config) { return new ArrayList<>(); }
    private static List<DiscoveredContent> crossDomainRecommendation(List<InterestTopic> interests, ContentDiscoveryConfig config) { return new ArrayList<>(); }
    private static List<DiscoveredContent> temporalAwareRecommendation(String userId, ContentDiscoveryConfig config) { return new ArrayList<>(); }
    private static List<DiscoveredContent> socialInfluenceRecommendation(String userId, ContentDiscoveryConfig config) { return new ArrayList<>(); }
    private static List<DiscoveredContent> aggregateAndRankContent(List<DiscoveredContent> exp, List<DiscoveredContent> cross, List<DiscoveredContent> temp, List<DiscoveredContent> social, ContentDiscoveryConfig config) { return new ArrayList<>(); }
    private static double calculateNoveltyScore(List<DiscoveredContent> content, String userId) { return 0.75; }
    private static ImageAnalysisResult analyzeImageContent(String imageUrl) { return new ImageAnalysisResult(); }
    private static List<String> generateObjectTags(List<String> objects) { return new ArrayList<>(); }
    private static List<String> generateSceneTags(String sceneType) { return new ArrayList<>(); }
    private static List<String> generateStyleTags(String style) { return new ArrayList<>(); }
    private static List<String> generateEmotionTags(String emotion) { return new ArrayList<>(); }
    private static List<String> generateColorTags(List<String> colors) { return new ArrayList<>(); }
    private static List<String> generateContextualTags(String imageUrl, TagRecommendationConfig config) { return new ArrayList<>(); }
    private static Map<String, Double> calculateTagWeights(List<String> obj, List<String> scene, List<String> style, List<String> emotion, List<String> color, List<String> context) { return new HashMap<>(); }
    private static List<RecommendedTag> filterAndRankTags(Map<String, Double> weights, TagRecommendationConfig config) { return new ArrayList<>(); }
    private static UserFeatureVector extractUserFeatures(String userId) { return new UserFeatureVector(); }
    private static List<UserSimilarity> calculateBehaviorSimilarity(UserFeatureVector features, UserSimilarityConfig config) { return new ArrayList<>(); }
    private static List<UserSimilarity> calculatePreferenceSimilarity(UserFeatureVector features, UserSimilarityConfig config) { return new ArrayList<>(); }
    private static List<UserSimilarity> calculateDemographicSimilarity(UserFeatureVector features, UserSimilarityConfig config) { return new ArrayList<>(); }
    private static List<UserSimilarity> fuseSimilarityScores(List<UserSimilarity> behavior, List<UserSimilarity> preference, List<UserSimilarity> demographic, UserSimilarityConfig config) { return new ArrayList<>(); }
    private static List<SimilarUser> rankSimilarUsers(List<UserSimilarity> similarities, UserSimilarityConfig config) { return new ArrayList<>(); }
    private static List<String> generateAutoCompletions(String query, SearchSuggestionConfig config) { return new ArrayList<>(); }
    private static List<String> generateRelatedQueries(String query, SearchSuggestionConfig config) { return new ArrayList<>(); }
    private static List<String> getTrendingQueries(SearchSuggestionConfig config) { return new ArrayList<>(); }
    private static List<String> generatePersonalizedSuggestions(String query, String userId, SearchSuggestionConfig config) { return new ArrayList<>(); }
    private static List<String> generateSpellCorrections(String query) { return new ArrayList<>(); }

    // 错误结果创建方法
    private static ImageSearchResult createImageSearchErrorResult(String error) { 
        ImageSearchResult result = new ImageSearchResult(); 
        result.setSuccess(false); 
        return result; 
    }
    private static SemanticSearchResult createSemanticSearchErrorResult(String error) { return new SemanticSearchResult(); }
    private static RecommendationResult createRecommendationErrorResult(String error) { return new RecommendationResult(); }
    private static ContentDiscoveryResult createContentDiscoveryErrorResult(String error) { return new ContentDiscoveryResult(); }
    private static TagRecommendationResult createTagRecommendationErrorResult(String error) { return new TagRecommendationResult(); }
    private static SimilarUserResult createSimilarUserErrorResult(String error) { return new SimilarUserResult(); }
    private static SearchSuggestionResult createSearchSuggestionErrorResult(String error) { return new SearchSuggestionResult(); }
}
